Processing of Biomedical Data with Machine Learning
- DOI
- 10.2991/csit-19.2019.2How to use a DOI?
- Keywords
- Machine Learning, Deep Learning, ECG, Biometric authentication, Cardiovascular diseases diagnosis
- Abstract
The paper is about processing of biomedical data. It were used 13 methods of machine learning (Naive Bayes classifier for multivariate Bernoulli models, A decision tree classifier, An extremely randomized tree classifier, Classifier implementing the k nearest neighbors vote, Linear Discriminant Analysis, Linear Support Vector Classification, Logistic Regression, Nearest centroid classifier, A random forest classifier, Classifier using Ridge regression, Ridge classifier with built in cross validation, Gaussian Mixture Models, Support Vector Machines) and one method of deep learning (Multiplayer Perception). A discrete wavelet transform was used to extract of biometric features. Haar wavelets, Daubechi wavelets, Symlets, Coiflets, Biorthogonal, Reverse biorthogonal, Discrete Meyer (FIR Approximation) were used. The influence of Electrocardiorams (ECG) recording time on the accuracy of biometric identification and diagnosis of cardiovascular diseases was studied. It was found that the best methods of classification are: Multiplayer Perception, An extremely randomized tree classifier, Classifier implementing the k nearest neighbors vote and Logistic Regression aka logit MaxEnt classifier. Wavelet family doesn’t affect significantly on accuracy of recognition. With increasing registration time, accuracy increases .
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Marat Bogdanov AU - Dajan Nasyrov AU - Irina Dumchikova AU - Artur Samigullin PY - 2019/12 DA - 2019/12 TI - Processing of Biomedical Data with Machine Learning BT - Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019) PB - Atlantis Press SP - 6 EP - 16 SN - 2589-4900 UR - https://doi.org/10.2991/csit-19.2019.2 DO - 10.2991/csit-19.2019.2 ID - Bogdanov2019/12 ER -